In this paper we investigate the discrete implication functions that satisfy the Modus Ponens with respect to a discrete uninorm. From this study, and analogously to the case of implication functions defined in the unit interval, it is obtained that the uninorms to be considered must be conjunctive. For this reason, we want to study the case of discrete implications derived from discrete uninorms. In particular, we investigate the U-conditionality for discrete residual implications derived from discrete uninorms in U-min and discrete idempotent uninorms.
As the usage of the edge-cloud continuum rises, Kubernetes presents itself as a solution that allows easy control and deployment of applications in these highly-distributed and heterogeneous environments. In this context, Artificial Intelligence methods have been proposed to aid in the task allocation process to optimize different aspects of the system, such as application execution time, load balancing or energy consumption. In this paper, we propose a space-time combinational model that uses Deep Reinforcement Learning (DRL) to recommend node allocations for Kubernetes pods with the objective of optimizing the overall energy consumption of the cluster while maintaining pod execution ratio. In particular, our approach uses Proximal Policy Optimization (PPO) with custom Neural Networks to train a DRL agent and includes a custom Kubernetes operator to enforce allocations based on the node recommendations generated by the agent. Using our custom solution, we performed a series of experiments with different workloads and compared the performance with the base Kubernetes scheduler. Our experimental results demonstrate a notable reduction of up to 24% in the energy consumption of the Kubernetes cluster.
Multiple sclerosis (MS) is a chronic autoimmune disease that predominantly affects the central nervous system (CNS) and is a leading cause of neurological disability among young adults. MS diagnosis heavily relies on clinical symptoms coupled with the detection of demyelinating lesions in the CNS, as depicted in conventional magnetic resonance images (MRI). These lesions may evolve over time and serve as critical biomarkers for assessing disease activity and the effectiveness of therapeutic interventions. Manual lesion delimitation in images is a tedious and error-prone process, prompting active research into automated systems. MS lesion segmentation can be conducted in two very different approaches: cross-sectionally: by using a single MRI, or longitudinally, by identifying changes such as new lesions or variations in lesion size across consecutives MRIs. Deep learning architectures such as U-Net, which employs convolutional neural networks with skip connections, have proven effective for MRI lesion segmentation. In this paper, we utilize the nnUNet v2 architecture and introduce an enhanced method capable of segmenting both cross-sectional and longitudinal MRIs. We used the ISBI 2015 dataset, training our U-Net model on a subset of patients using a generative pipeline and testing on another subset of patients from the same dataset. For training the model, we used a Bayesian generative approach to create different synthetic data simulating random variations in lesion masks. In this process, we have randomly simulated multiple temporal variations of the lesion masks, including erosion, dilation, and the removal of individual lesions within each mask. Then, we adapted the U-Net to train using a single MRI and its corresponding synthetic segmentation, which simulates the lesions in a previous time point. Thanks to the large amount of synthetic data generated, the U-Net is able to learn the intrinsic behavior of the lesions which include expansion, shrinkage, appearance and disappearance. This approach enables, for the first time, the two classical operational modes for MS lesion segmentation within a single model: cross-sectional segmentation, where the model receives a MRI and an empty mask as input, and longitudinal segmentation, where the input includes a MRI and a mask from a previous time point. Our approach achieved a Dice Similarity Coefficient (DSC) of 0.75 for cross-sectional segmentation and 0.81 for longitudinal segmentation, indicating improved performance when incorporating temporal information.This study demonstrates that it is possible to apply a generative method in conjunction with a U-Net architecture for both cross-sectional and longitudinal MRI lesion segmentation in MS, yielding promising results.
Stress, the body’s reaction to threatening situation, may derive in negative physical and mental health effects. Since speech under stress (SuS) shows relevant changes in the voice production system, its analysis may become instrumental to identify stressful situations non-invasively. Despite most investigations have focused on identifying SuS from prosodic and spectral variations, preliminary attempts have evaluated the alterations of the glottal source. This work evaluates the viability of considering glottal source features for SuS classification. Results on the cardinal vowels extracted from VOCE corpus show classification accuracies between 90.5% and 92.3% when glottal source features are combined with Mel Frequency Cepstral Coefficients to identify SuS, improving results in 6 out of 8 the SVM and NN two-class classifiers when trained with them separately.
Breast density is a crucial biomarker for predicting BC risk and recurrence. Women with dense breast tissues have a higher likelihood of developing BC, and dense tissue can obscure lesions, reducing detection sensitivity. Mammograms are vital for evaluating breast density, typically classified using the BI-RADS system. The main challenge in breast density segmentation is accurately localizing dense tissues. While segmentation models require detailed pixel-wise annotations, obtaining these labels is time-consuming and requires medical expertise. This paper proposes a weakly supervised approach for breast density localization, allowing deep neural network classifiers to generate saliency maps that highlight dense tissue regions based on image-level labels. We validate this model on the RSNA dataset and achieve a Dice score of 0.754, comparable to state-of-the-art supervised methods.
